Handheld Smart Devices Effects on Postural Muscles Related to Upper Cross Syndrome
Faris Shuleih Alshammari1 and Eman Salameh Alzoghbieh2
1Doctor of Physical Therapy Program, University of Saint Augustine for Health
Sciences, San Marcos, CA, USA
2School of Natural Sciences and Human Ecology, California State University, San
Bernardino, CA, USA
*Corresponding Author: Eman Salameh Alzoghbieh, School of Natural Sciences and Human Ecology, California State University, San Bernardino, CA, USA.
December 27, 2022; Published: January 25, 2023
Background: Upper Cross Syndrome (UCS) is a major postural disorder. Postural muscles changes can be related to sustained prolonged poor posture. As time spent on handheld smart devices (TSSD) is increasing yearly, there is a necessity to study its effects on body posture. Limited literature was found on the effect of TSSD on body posture.
Objectives: The purpose of this study was to examine the relationship between the prevalence of muscular changes associated with UCS and the TSSD among university students in Jordan.
Methods: Two hundred participants were recruited for this study. After obtaining the informed consents, Participants filled a survey and underwent standardized measures including head position measures, Manual Muscles Testing (MMT) and Muscles Length Test. Survey included multiple factors that can be related to postural changes.
Results: There was association between the prevalence of the muscular changes associated with UCS and time spent on the use of handheld smart devices where 8.7% of students who used handheld smart devices for less than one hour a day developed factors that lead to UCS. Marked increase on the prevalence of the muscular changes associated with UCS to be 70.4% among the students who used the handheld smart devices from one hour to three hours a day. The prevalence of the muscular changes associated with UCS was the highest among the students who used the handheld smart devices for more than 3 hours to be 89.1%.
Conclusion: Limiting time spent on handheld smart devices to one hour daily can be helpful in maintaining healthy body posture and physical well-being.
Clinical Implications: Physical therapists can use the findings of this study to educate their patients who suffer from upper quarter muscles dysfunction on the importance of controlling the time spent on handheld smart devices.
Keywords: Body Posture; Handheld smart Devices; Upper Cross Syndrome; Upper Quarter; Time
- WCHN, Good posture - looking after your back. women's and children's health network (2016).
- Alattas R. “Postuino. Bad Posture Detector using Arduino”. International Journal of Innovation and Scientific Research2 (2014): 208-212.
- Muscolino J. “Upper Cross Syndrome”. Journal of the Australian Traditional-Medicine Society2 (2015): 80-85.
- Morris CE., et al. “The Torsional Upper Crossed Syndrome: A multi-planar update to Janda's model, with a case series introduction of the mid-pectoral fascial lesion as an associated etiological factor”. Journal of Bodywork and Movement Therapies 4 (2015): 681-689.
- Gu SY., et al. “Relationship between position sense and reposition errors according to the degree of upper crossed syndrome”. The Journal of Physical Therapy Science 2 (2016): 438-41.
- Kreighbaum E and K Barthels. “Biomechanics; A Qualitative Approach for Studying Human Movement Allyn and Bacon A Pearson Education Company (1996).
- Charles A., et al. “Head and shoulder posture affect scapular mechanics and muscle activity in overhead tasks”. Journal of Electromyography and Kinesiology4 (2010): 701-709.
- Iqra Mubeen., et al. “PREVALENCE OF UPPER CROSS SYNDROME AMONG THE MEDICAL STUDENTS OF UNIVERSITY OF LAHORE”. International Journal of Physiotherapy3 (2016): 381-384.
- Koh MJ., et al. “Assessing the Prevalence of Recurrent Neck and Shoulder Pain in Korean High School Male Students: A Cross-sectional Observational Study”. The Korean Journal of Pain 3 (2012): 161-167.
- Gaskin DJ and P Richard. “The economic costs of pain in the United States”. Journal of Pain 8 (2012): 715-724.
- Leap P. “Another Way to Improve your Digestion”. Posture (2018).
- Erik Peper P and I-Mei Lin. “Increase or Decrease Depression: How Body Postures Influence Your Energy Level”. Association for Applied Psychophysiology and Biofeedback3 (2012): 125-130.
- Kang JH., et al. “The effect of the forward head posture on postural balance in long time computer based worker”. Annals of Rehabilitation Medicine 1 (2012): 98-104.
- Statista, Unit sales of smart devices worldwide by category worldwide from 2013 to 2020 (2017).
- Statista, Daily time spent with the internet per capita worldwide from 2011 to 2021, by device (2020).
- DATAPORTAL, Digital 2020: Global Digital Overview (2020).
- “The data on device usage (2020).
- “Use of smartphones and social media is common across most emerging economies” (2019).
- Kee IK., et al. “The presence of altered craniocervical posture and mobility in smartphone-addicted teenagers with temporomandibular disorders”. The Journal of Physical Therapy Science 2 (2016): 339-346.
- Jung, SI., et al. “The effect of smartphone usage time on posture and respiratory function”. The Journal of Physical Therapy Science 1 (2016): 186-189.
- Kang KW., et al. “Effect of sitting posture on respiratory function while using a smartphone”. The Journal of Physical Therapy Science 5 (2016): 1496-1498.
- Toh SH., et al. “The associations of mobile touch screen device use with musculoskeletal symptoms and exposures: A systematic review”. PLoS One 8 (2017): e0181220.
- Audette I., et al. “Validity and between-day reliability of the cervical range of motion (CROM) device”. Journal of Orthopaedic and Sports Physical Therapy 5 (2010): 318-323.
- Hislop H and J Montgomery. “Muscle Testing: Techniques of Manual Examination”. 6th Daniels and Worthingham's (1995).
- Melissa G Hunt., et al. “No More Fomo: Limiting Social Media Decreases Loneliness and Depression”. Journal of Social and Clinical Psychology10 (2018): 751-768.
- Levenson JC., et al. “The association between social media use and sleep disturbance among young adults”. Preventive Medicine 85 (2016): 36-41.
- Shampa Das RD and Aman Kumar Shamp. “COMPUTER VISION SYNDROME AND ITS RISK FACTORS AMONG PROFESSIONAL COLLEGE STUDENTS OF AGARTALA: A CROSS SECTIONAL STUDY 5 (2016): 27-29.
- Shantakumari N., et al. “Computer use and vision-related problems among university students in ajman, United arab emirate”. Annals of Medical and Health Science Research 2 (2014): 258-263.
- Logaraj M., et al. “Computer vision syndrome and associated factors among medical and engineering students in chennai”. Annals of Medical and Health Science Research 2 (2014): 179-185.
- Khola Noreen ZB., et al. “Prevalence of Computer Vision Syndrome and Its Associated Risk Factors among Under Graduate Medical Students of Urban Karach”. Pakistan Journal of Ophthalmology3 (2016).